With the implementation of several new earth observation plans and the ability of large-scale cloud computing, more satellite imageries are available and have been used to generate land surface monitor products. In this channel, we have selected several famous agricultural datasets achieved from satellite data to present the agricultural resources, monitor crop growth. The datasets are cataloged as: - Agricultural land cover - Several major Land surface parameters - Land Surface phenology and crop calendar - Disease and disaster

MLRSNet is a a multi-label high spatial resolution remote sensing dataset for semantic scene understanding. It provides different perspectives of the world captured from satellites. That is, it is composed of high spatial resolution optical satellite images. MLRSNet contains 109,161 remote sensing images that are annotated into 46 categories, and the number of sample images in a category varies from 1,500 to 3,000. The images have a fixed size of 256×256 pixels with various pixel resolutions (~10m to 0.1m). Moreover, each image in the dataset is tagged with several of 60 predefined class labels, and the number of labels associated with each image varies from 1 to 13. The dataset can be used for multi-label based image classification, multi-label based image retrieval, and image segmentation.

Context

The image comes from the remote sensing image data obtained by ZY-3 satellite. The spatial resolution is 2 meters, and the spectrum is in the visible light band (red, green and blue).

Content

Eight images and corresponding cultivated land labels are used for model training and testing of contestants. The images are in TIF format. The preview image of the original image is shown in Figure 1, and the preview image of the label image is shown in Figure 2. In the label image, white (with a value of 1) represents the cultivated land class, and black (with a value of 0) represents the background class. Another two images are provided as test examples.

Acknowledgements

Data source: the data of this event is from the MathorCup Big Data Competition in 2021. The data are all from the public information of the website, and do not contain any personal privacy data. Thank you for your data work. More details can can be found at http://www.mathorcup.org/

Inspiration

The Remote Sensing Image Captioning Dataset (RSICD) is a dataset for remote sensing image captioning task. It contains more than ten thousands remote sensing images which are collected from Google Earth, Baidu Map, MapABC and Tianditu. The images are fixed to 224X224 pixels with various resolutions. The total number of remote sensing images is 10921, with five sentences descriptions per image.

Context

Satellite image Classification Dataset-RSI-CB256 , This dataset has 4 different classes mixed from Sensors and google map snapshot

Content

The past years have witnessed great progress on remote sensing (RS) image interpretation and its wide applications. With RS images becoming more accessible than ever before, there is an increasing demand for the automatic interpretation of these images. In this context, the benchmark datasets serve as essential prerequisites for developing and testing intelligent interpretation algorithms. After reviewing existing benchmark datasets in the research community of RS image interpretation, this article discusses the problem of how to efficiently prepare a suitable benchmark dataset for RS image interpretation. Specifically, we first analyze the current challenges of developing intelligent algorithms for RS image interpretation with bibliometric investigations. We then present the general guidance on creating benchmark datasets in efficient manners. Following the presented guidance, we also provide an example on building RS image dataset, i.e., Million-AID, a new large-scale benchmark dataset containing a million instances for RS image scene classification. Several challenges and perspectives in RS image annotation are finally discussed to facilitate the research in benchmark dataset construction. We do hope this paper will provide the RS community an overall perspective on constructing large-scale and practical image datasets for further research, especially data-driven ones.

Acknowledgements

Annotated Datasets for RS Image Interpretation The interpretation of RS images has been playing an increasingly important role in a large diversity of applications, and thus, has attracted remarkable research attentions. Consequently, various datasets have been built to advance the development of interpretation algorithms for RS images. Covering literature published over the past decade, we perform a systematic review of the existing RS image datasets concerning the current mainstream of RS image interpretation tasks, including scene classification, object detection, semantic segmentation and change detection.

Inspiration

Artificial Intelligence, Computer Vision, Image Processing, Deep Learning, Satellite Image, Remote Sensing

The dataset was created from high-resolution, true-color satellite imagery of Pleiades-1A acquired on March 15, 2017. Pleiades is an Airbus product that provides imagery with a 0.5m resolution at different spectral combinations. A total of 110 patches of size 600×600 pixels were selected by visually eyeballing random locations in the city that contain a wide variety of urban characteristics such as vegetation, slums, built-up, roads, etc. The patches were manually annotated with polygons using Intel’s Computer Vision Annotation Tool (CVAT). Six unique classes were used to categorize the images, namely (1) vegetation; (2) built-up; (3) informal settlements; (4) impervious surfaces (roads/highways, streets, parking lots, road-like areas around buildings, etc.); (5) barren; and (6) water. In addition to these six major classes, the dataset also contains another class termed ‘Unlabelled’, which makes up only 0.08% of the dataset. It primarily consists of airplanes and a few other obscure spots and structures. Each 600×600 pixels patch was further divided into 120×120 pixels sized tiles with 50% horizontal and vertical overlapping, making a total of 8910 tiles. This helped generate more training data that would result in better classification. Out of the total 8910 annotated patches, 80% of patches (total: 7128) are present in the training set, 10% as the validation set (total: 891), and the remaining 10% for testing (total: 891).

Accurate flood delineation is crucial in many disaster management tasks, including, but not limited to: risk map production and update, impact estimation, claim verification, or planning of countermeasures for disaster risk reduction. Open remote sensing resources such as the data provided by the Copernicus ecosystem enable to carry out this activity, which benefits from frequent revisit times on a global scale. In the last decades, satellite imagery has been successfully applied to flood delineation problems, especially considering Synthetic Aperture Radar (SAR) signals. However, current remote mapping services rely on time-consuming manual or semi-automated approaches, requiring the intervention of domain experts. The implementation of accurate and scalable automated pipelines is hindered by the scarcity of large-scale annotated datasets. To address these issues, we propose MMFlood, a multimodal remote sensing dataset purposely designed for flood delineation. The dataset contains 1748 Sentinel-1 acquisitions, comprising 95 flood events distributed across 42 countries. Together with satellite imagery, the dataset includes the Digital Elevation Model (DEM), hydrography maps, and flood delineation maps provided by Copernicus EMS, which is considered as ground truth. We release MMFlood, comparing its relevance with similar earth observation datasets. Moreover, to set baseline performances, we conduct an extensive benchmark of the flood delineation task using state-of-art deep learning models, and we evaluate the performance gains of entropy-based sampling and multi-encoder architectures, which are respectively used to tackle two of the main challenges posed by MMFlood, namely the class unbalance and the multimodal setting.

A 12-chapter introductory eBook about remote sensing. Contents cover (1) Remote Sensing and Geospatial Technology, (2) Energy and Radiation, (3) Airphotos, (4) Digital Imagery, (5) Satellite, Landsat, and MODIS, (6) Preparing Images for Analysis, (7) Band Transformation, (8) Visual Enhancement, (9) Image Classification, (10) Microwave Remote Sensing, (11) Thermal Remote Sensing, and (12) Remote Sensing with Drones. Last update: 2020.

The global remote sensing services market size reached US$ 15.7 billion in 2022. Revenue generated by remote sensing system sales is likely to be US$ 18.4 billion in 2023. Sales are poised to soar by 14.0% CAGR over the forecast period between 2023 and 2033. Demand is anticipated to transcend at US$ 68.0 billion by 2033 end.

Attributes	Key Insights
Remote Sensing Services Market Estimated Size (2023E)	US$ 18.4 billion
Remote Sensing Services Market Projected Valuation (2033F)	US$ 68.0 billion
Value-based CAGR (2023 to 2033)	14.0%

Historical Performance of Remote Sensing Services Market

Historical CAGR of Remote Sensing System Market (2018 to 2022)	17.6%
Historical Value of Remote Sensing Systems Market (2022)	US$ 15.7 billion

Country-wise Insights

Country	Projected Value (2033)
United States	US$ 12.2 billion
United Kingdom	US$ 2.7 billion
China	US$ 10.4 billion
Japan	US$ 7.4 billion
South Korea	US$ 4.2 billion

Category-wise Insights

Category	Satellites
Value-based CAGR (2023 to 2033)	13.8%

Category	Spatial
Value-based CAGR (2023 to 2033)	13.6%

Scope of the Report

Attribute	Details
Estimated Market Size (2023)	US$ 18.4 billion
Projected Market Valuation (2033)	US$ 68.0 billion
Value-based CAGR (2023 to 2033)	14.0%
Forecast Period	2023 to 2033
Historical Data Available for	2018 to 2022
Market Analysis	Value (US$ billion/million) and Volume (MT)
Key Regions Covered	Latin America, North America, Europe, South Asia, East Asia, Oceania, and Middle East & Africa
Key Countries Covered	United States, Mexico, Brazil, Chile, Peru, Argentina, Germany, France, Italy, Spain, Canada, United Kingdom, Belgium, Nordic, Poland, Russia, Japan, South Korea, China, Netherlands, India, Thailand, Malaysia, Indonesia, Singapore, Australia, New Zealand, GCC Countries, South Africa, Central Africa, and others
Key Market Segments Covered	Platform Resolution End-use Region
Key Companies Profiled	Antrix Corporation Ltd Cyberswift LLC DigitalGlobe Ekofastba S.L Geo Sense Sdn. Bhd

The Global Remote Sensing Services Market in terms of revenue was estimated to be worth USD 13.2 billion in 2022 and is poised to reach USD 26.6 billion by 2027, growing at a CAGR of 14.9% from 2022 to 2027. The new research study consists of an industry trend analysis of the market. Report Scope

Market size available for years

2018–2027

Base year considered

2022

Forecast period

2022-2027

Forecast units

Value (USD Million/Billion)

Segments covered

By Application, By Platform, By End Use, By Technology, By Resolution, By Type

Geographies covered

North America, Europe, Asia Pacific, Middle East & Africa, and Latin America

Companies covered

Maxar technologies (US), Planet Labs PBC (US), L3 Harris Technologies (US), Airbus SE (Netherlands), Trimble, Inc. (US) are some of the major players of Remote Sensing Services market. (25 Companies)

The study categorizes the Remote Sensing Services market based on application, platform, end use, resolution, technology, type and region.

By Application Agriculture Engineering & Infrastructure Environmental & Weather Energy & power Transportation & Logistic Defense & Security Maritime Insurance Academic & Research Others By Platform Satellite UAVs Manned Aircraft Ground By End Use Military & Government Commercial By Resolution Spatial Spectral Radiometric Temporal By Technology Active Passive By Type Aerial Photography & Remote Sensing Data Acquisition & Analytics By Region North America Asia Pacific Europe Middle East & Africa Latin America

Table of Contents

• Market Overview
• Key Takeaways
• Technology Analysis
• Application Analysis
• Platform Analysis
• Driving Factor
• Restraining Factor
• Growth Opportunity
• Key Market Trend
• Key Market Segments
• Regional Analysis
• Key Players Analysis
• Recent development
• Report Scope

Market Overview

The Global Remote Sensing Technology Market size is expected to be worth around USD 61.7 Billion by 2033 from USD 19.7 Billion in 2023, growing at a CAGR of 19% during the forecast period from 2023 to 2032.

Remote sensing technology plays a crucial role in acquiring information and data about the Earth’s surface and atmosphere from a distance, impacting various facets of life. In the realm of agriculture, remote sensing technology enhances crop management by furnishing insights into crop health, moisture levels, and soil characteristics. This data is instrumental in optimizing irrigation and fertilizer application, leading to reduced water consumption and enhanced crop yields. Read More

This dataset contains satellite imagery of 4,454 power plants within the United States. The imagery is provided at two resolutions: 1m (4-band NAIP iamgery with near-infrared) and 30m (Landsat 8, pansharpened to 15m). The NAIP imagery is available for the U.S. and Landsat 8 is available globally. This dataset may be of value for computer vision work, machine learning, as well as energy and environmental analyses.Additionally, annotations of the specific locations of the spatial extent of the power plants in each image is provided. These annotations were collected via the crowdsourcing platform, Amazon Mechanical Turk, using multiple annotators for each image to ensure quality. Links to the sources of the imagery data, the annotation tool, and the team that created the dataset are included in the "References" section.To read more on these data, please refer to the "Power Plant Satellite Imagery Dataset Overview.pdf" file. To download a sample of the data without downloading the entire dataset, download "sample.zip" which includes two sample powerplants and the NAIP, Landsat 8, and binary annotations for each.Note: the NAIP imagery may appear "washed out" when viewed in standard image viewing software because it includes a near-infrared band in addition to the standard RGB data.

The dataset comprises of 92 satellite images containing 61 training set, 21 validation set and 10 testing set, together with the annotation files. Sample images from testing dataset are presented in Fig. 3. The images in the dataset were annotated using the roboflow application. The dataset contains 5 categories of objects: residence, roads, shoreline, swimming pool and vegetation. The dataset can be used to train, validate and test variants of YOLO models [2] for object detection from remote sensing satellite images.

The global remote sensing technology market size was valued at USD 17.53 billion in 2022 and is expected to grow at a CAGR of 11.6% from 2023 to 2030

The Report Covers Remote Sensing Technology Market Analysis and it is segmented by Application (Earth Observation, Meteorology, Mapping and Navigation), End-user (Defense, Commercial), and Geography (North America, Europe, Asia-Pacific, Latin America, and Middle East and Africa). The Market Size and Forecasts Are Provided in Terms of Value (USD Million) for All the Above Segments

Mapping millions of buried landmines rapidly and removing them cost-effectively is supremely important to avoid their potential risks and ease this labour-intensive task. Deploying uninhabited vehicles equipped with multiple remote sensing modalities seems to be an ideal option for performing this task in a non-invasive fashion. This report provides researchers with vision-based remote sensing imagery datasets obtained from a real landmine field in Croatia that incorporated an autonomous uninhabited aerial vehicle (UAV), the so-called LMUAV. Additionally, the related knowledge regarding the literature survey is presented to guide the researchers properly. More explicitly, two remote sensing modalities, namely, multispectral and long-wave infrared (LWIR) cameras were mounted on an advanced autonomous UAV and datasets were collected from a well-designed field containing various types of landmines. In this report, multispectral imagery and LWIR imagery datasets are presented for researchers who can fuse these datasets using their bespoke applications to increase the probability of detection, decrease the false alarm rate, and most importantly, improve their techniques based on the features of vision-based imagery datasets.

Dataset from the paper Change Event Dataset for Discovery from Spatio-temporal Remote Sensing Imagery. It contains ~32000 change events from the city of Cairo and state of California. IT also contains labelled examples of forest fires and road constructions.

The Remote Sensing Division is responsible for providing data to support the Coastal Mapping Program, Emergency Response efforts, and the Aeronautical Survey Program through the use of remotely sensed data. NOAA Coastal Mapping Remote Sensing Data includes metric-quality aerial photographs from film and digital cameras, orthomosaics, and Light Detection and Ranging (lidar). The predecessors to the Remote Sensing Division began using single-lens aerial photography in 1943. The single-lens and digital aerial photographs are maintained by the National Geodetic Survey. The applications include shoreline delineation, mapping water depths, topographic mapping, mapping seabed characteristics, locating features or obstructions to ensure the safety of marine and air navigation, and to support NOAA's homeland security and emergency response requirements. Orthomosaics and lidar data sets are available from the NOAA Office for Coastal Management's DigitalCoast. The National Geodetic Survey acquires remotely sensed data in the coastal realm including the Great Lakes and their connecting navigable waterways and select areas to support homeland security and emergency response.

details of Remote Sensing inpections undertaken